There are various known methods for detecting feature points in an image based on the result of methods based on performing spatial differentiation on an image, called “Harris”, “KLT” and “Structure Multivector”. These methods are for superimposing one or more spatial differential filters on an image or methods using a combination of spatial differential filters, and some operation for their combination and determination of a feature point position.
These methods are described in detail in the following documents.
C. Harris and M. J. Stephens, “A Combined Corner and Edge Detector”, In Alvey 88, pages 147-152, 1988
J. Shi and C. Tomasi, “Good features to track”, IEEE Conference on Computer Vision and Pattern Recognition, 593-600, 1994
M. Felsberg and G. Sommer, “Image Features Based on a New Approach to 2D Rotation Invariant Quadrature Filters”, European Conference on Computer Vision, 369-383, 2002.
In these methods, a feature point in an image is detected by the combination of the obtained spatial differential values. The feature points detected by these methods generally correspond to a portion (corner point) where an edge exhibits significant curvature.
The result of detection of feature points according to such prior-art methods is subject to variation due to the contrast or S/N ratio of an image. It is therefore difficult to detect feature points accurately and stably under various conditions. When, for example, the fractional noise amount differs from one image to another or from one local region of one image relative to another, it is difficult to detect feature points correctly and stably.
An optimum detection threshold value corresponding to the noise amount has to be determined manually for each image or for each local region. Thus, a large amount of labor is required for processing a large number of images.
It has therefore been desired to realize an image processing method in which feature points can be always detected accurately independently of the influence of any noise present in an image.